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arXiv 2006.16951astro-ph.SRastro-ph.EPastro-ph.IM
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双星系统的物理学V.解决逆问题的一般框架

Physics of Eclipsing Binaries. V. General Framework for Solving the Inverse Problem

Kyle E Conroy, Angela Kochoska, Daniel Hey, Herbert Pablo, Kelly M Hambleton, David Jones, Joseph Giammarco, Michael Abdul-Masih, Andrej Prsa

摘要:

PHOEBE 2是一个Python包,用于对食星系统的可观测量进行建模,但到目前为止,它完全集中在前向模型上-也就是说,在给定大量描述系统和观测的参数的固定值的情况下,生成一个合成模型。逆问题,获得轨道和恒星参数的观测数据,是更复杂和计算昂贵的,因为它需要产生一个大的前向模型,以确定哪一组参数和不确定性最能代表现有的观测数据。确定最佳解决方案以及获得这些参数的可靠和鲁棒不确定性的过程通常需要使用多种算法,包括优化器和采样器。此外,PHOEBE的前向模型已被设计为尽可能物理上稳健,但与其他代码相比,计算成本较高。因此,在给定特定系统的合理假设的情况下,使用最有效的代码是有用的,但是学习多个代码的复杂性在实践中对这样做构成了障碍。在这里,我们介绍了PHOEBE的2.3版本(可从http://phoebe-project.org公开获得),它引入了一个通用框架,用于定义和处理参数分布,并利用多种不同的估计,优化和采样算法。所提出的框架支持多个前向模型,包括内置到PHOEBE本身的强大模型。

英文摘要:

PHOEBE 2 is a Python package for modeling the observables of eclipsing star systems, but until now has focused entirely on the forward-model -- that is, generating a synthetic model given fixed values of a large number of parameters describing the system and the observations. The inverse problem, obtaining orbital and stellar parameters given observational data, is more complicated and computationally expensive as it requires generating a large set of forward-models to determine which set of parameters and uncertainties best represent the available observational data. The process of determining the best solution and also of obtaining reliable and robust uncertainties on those parameters often requires the use of multiple algorithms, including both optimizers and samplers. Furthermore, the forward-model of PHOEBE has been designed to be as physically robust as possible, but is computationally expensive compared to other codes. It is useful, therefore, to use whichever code is most efficient given the reasonable assumptions for a specific system, but learning the intricacies of multiple codes presents a barrier to doing this in practice. Here we present the 2.3 release of PHOEBE (publicly available from http://phoebe-project.org) which introduces a general framework for defining and handling distributions on parameters, and utilizing multiple different estimation, optimization, and sampling algorithms. The presented framework supports multiple forward-models, including the robust model built into PHOEBE itself.

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